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Constructing Complex 3D Biological Environments from Medical Imaging Using High Performance Computing
May-June 2012 (vol. 9 no. 3)
pp. 643-654
D. Walker, Dept. of Comput. Sci., Univ. of Sheffield, Sheffield, UK
M. Burkitt, Dept. of Comput. Sci., Univ. of Sheffield, Sheffield, UK
D. M. Romano, Dept. of Comput. Sci., Univ. of Sheffield, Sheffield, UK
A. Fazeli, Dept. of Human Metabolism, Univ. of Sheffield, Sheffield, UK
Extracting information about the structure of biological tissue from static image data is a complex task requiring computationally intensive operations. Here, we present how multicore CPUs and GPUs have been utilized to extract information about the shape, size, and path followed by the mammalian oviduct, called the fallopian tube in humans, from histology images, to create a unique but realistic 3D virtual organ. Histology images were processed to identify the individual cross sections and determine the 3D path that the tube follows through the tissue. This information was then related back to the histology images, linking the 2D cross sections with their corresponding 3D position along the oviduct. A series of linear 2D spline cross sections, which were computationally generated for the length of the oviduct, were bound to the 3D path of the tube using a novel particle system technique that provides smooth resolution of self-intersections. This results in a unique 3D model of the oviduct, which is grounded in reality. The GPU is used for the processor intensive operations of image processing and particle physics based simulations, significantly reducing the time required to generate a complete model.

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Index Terms:
virtual reality,biological organs,biological tissues,biomedical MRI,biomedical ultrasonics,computerised tomography,feature extraction,graphics processing units,gynaecology,medical image processing,splines (mathematics),particle physics,complex 3D biological environments,medical imaging,high performance computing,information extraction,biological tissue,static image data,CPU,GPU,mammalian oviduct,fallopian tube,histology images,3D virtual organ,3D path determination,linear 2D spline cross sections,resolution,self-intersections,image processing,Three dimensional displays,Graphics processing unit,Electron tubes,Solid modeling,Computational modeling,Biological systems,histology.,GPU,particle system,image processing,geometric reconstruction,biological tissue
Citation:
D. Walker, M. Burkitt, D. M. Romano, A. Fazeli, "Constructing Complex 3D Biological Environments from Medical Imaging Using High Performance Computing," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 643-654, May-June 2012, doi:10.1109/TCBB.2011.69
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